Face and other object tracking in off-center peripheral regions for nonlinear lens geometries

ABSTRACT

A technique of enhancing a scene containing one or more off-center peripheral regions within an initial distorted image captured with a large field of view includes determining and extracting an off-center region of interest (hereinafter “ROI”) within the image. Geometric correction is applied to reconstruct the off-center ROI into a rectangular or otherwise undistorted or less distorted frame of reference as a reconstructed ROI.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part (CIP) of U.S. patentapplication Ser. No. 13/077,891, filed Mar. 31, 2011. This applicationis also related to U.S. Ser. No. 13/077,936, also filed Mar. 31, 2011.This application is also related to U.S. Ser. Nos. 12/959,089,12/959,137 and 12/959,151, each filed Dec. 2, 2010. All of theseapplications belong to the same assignee and are incorporated byreference. Another related application by the same assignee and sameinventors, entitled FACE AND OTHER OBJECT DETECTION AND TRACKING INOFF-CENTER PERIPHERAL REGIONS FOR NONLINEAR LENS GEOMETRIES (DocketFN-371B-US), is filed contemporaneously with the present application.

BACKGROUND

Images produced by a wide field of view lens vary in quality dependingon the field angle. It is a physical limitation of such a lens.

Wide Field of View System

A WFOV, fish-eye or similar non-linear imaging system incorporates alens assembly and a corresponding image sensor which is typically moreelongated than a conventional image sensor. An indicative embodiment isprovided in FIG. 1. The system may incorporate a face tracking modulewhich employs one or more cascades of rectangular face classifiers.

Non-Linear Lens Geometries

An example expanded view of such a non-linear lens geometry isillustrated in FIG. 2. We note that some lens constructions can bemodified to enhance the resolution of peripheral regions as described inU.S. Pat. No. 5,508,734 to Baker et al. However even with suchmodifications of the lens structure there is still a difference inresolution between the inner and outer regions of a non-linear lens whenthe imaged scene is projected onto the imaging sensor.

Distortion

Taking a typical lens to sensor mapping of a rectangular grid will yielda pattern similar to FIG. 3. FIG. 3 illustrates distortion of arectangular pattern caused by a typical non-linear (fish-eye) lens.Other patterns exist as illustrated in FIGS. 4( a)-4(i).

The radial distortion patterns are easier to manufacture and most lensesused in consumer imaging will exhibit one of the radial distortionpatterns illustrated in FIGS. 3 and 4( a)-4(i). Image distortions may becorrected using various geometrical correction engines. These enginestypically modify the pixels obtained at the sensor and transform theminto a corrected rectangular grid. Such distortions may be correctedaccording to one particular application, which is to implement avariable electronic zoom by scaling the window of pixels used toreconstruct a rectangular image. FIG. 5 schematically illustrates twodifferent windows used to build an ×1 and ×2.5 zoom image from the sameset of underlying pixels data. Only the central pixels are used for thehigher zoom 602 a. FIG. 5 illustrates regions of an image sensor used toconstruct electronic zoom at ×1.0 (601 a) and ×2.5 (602 a)magnification.

Global Motion In Non-Linear Lens Geometries

Global motion can affect and induce errors in such an imaging system.This is illustrated in FIGS. 6 & 7( a)-7(b). US Patent application2005/0196068 to Kawai details an improvement of such imaging systems tocompensate for global motion and correct the geometrical corrections forcamera motion during image acquisition. The imaging system of Kawaiincorporates a vibration detecting subsystem but this could be replacedby various alternative motion detecting subsystems, including aframe-to-frame alignment engine operative solely on image data acquiredin a video sequence. FIG. 6: Global motion (camera movement/hand-shake)lead to errors in geometrical correction; this will be more emphasizedat higher focus factors. FIG. 7 illustrates motion vectors arising fromglobal motion are more emphasized towards the center of a typicalnon-linear lens (RHS), whereas they are uniform across a conventional(linear) lens.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor.

Copies of this patent or patent application publication with colordrawing(s) will be provided by the Office upon request and payment ofthe necessary fee.

FIG. 1 illustrates a wide field of view (WFOV) system incorporatingface-tracker.

FIG. 2 illustrates an exploded diagram of a non-linear lens.

FIG. 3 illustrates distortion of a rectangular pattern caused by atypical non-linear (fish-eye) lens.

FIGS. 4( a)-4(i) illustrate various non-linear distortion patterns for arectangular grid mapped onto an imaging sensor.

FIG. 5 illustrates regions of an image sensor used to constructelectronic zoom at ×1.0 (601 a) and ×2.5 (602 a) magnification.

FIG. 6 illustrates global motion (camera movement/hand-shake) that tendsto lead to errors in geometrical correction; this will be moreemphasized at higher focus factors.

FIGS. 7( a) and 7(b) illustrate motion vectors arising from globalmotion are more emphasized towards the center of a typical non-linearlens (RHS), whereas they are uniform across a conventional (linear)lens.

FIGS. 8( a) and 8(b) illustrate three different 4×3 ROIs within the FOVof the non-linear lens of (i) an exemplary fish-eye imaging system and(ii) an exemplary non-linear WFOV imaging system.

FIG. 9 illustrates an expanded image frame with different reconstructedimage quality in the regions 1, 2, 3.

FIG. 10 Sequence of ROIs tracked across the imaging sensor acquired as asequence of video frames.

FIG. 11( a) illustrates a wide horizontal scene mapped onto a fullextent of an image sensor.

FIG. 11( b) illustrates a wide horizontal scene not mapped onto a fullextent of an image sensor, and instead a significant portion of thesensor is not used.

FIG. 12 illustrates the first four Haar classifiers.

DETAILED DESCRIPTIONS OF THE EMBODIMENTS

Within an image acquisition system comprising a non-linear, wide-angledlens and an imaging sensor, a method is provided to enhance a scenecontaining one or more off-center peripheral regions. An initialdistorted image is acquired with a large field of view using anon-linear, wide-angled lens and imaging sensor. An off-center region ofinterest (hereinafter “ROI”) is determined and extracted within theimage. Geometric correction is applied to reconstruct the off-center ROIinto an approximately rectangular frame of reference as a firstreconstructed ROI. A quality of reconstructed pixels is determinedwithin the first reconstructed ROI. Object tracking is applied to one ormore regions within the first reconstructed ROI, respectively adapted toone or more local reconstructed pixel qualities within the one or moreregions. The method also includes determining if undetected objectsbelow a predetermined size threshold are likely to exist in one or morereduced quality regions of the first reconstructed ROI. Responsive tothe determining if undetected objects exist, one or more additionalinitially distorted images are acquired, and the method includesextracting and reconstructing matching additional ROIs to combine withreduced quality pixels of the first reconstructed ROI to provide one ormore enhanced ROIs. A further action is performed based on a value of aparameter of an object within the one or more enhanced ROIs.

The method may include using a super-resolution technique to generate atleast one of the one or more enhanced ROIs.

The method may include applying additional object tracking to theenhanced ROI to confirm a location of a object below the predeterminedsize threshold.

The method may include compensating for global motion of the imagingdevice.

The parameter of the object may be or may include location.

Prior to the applying geometric correction, an initial object detectionprocess may be applied to the off-center ROI. The applying geometriccorrection may be performed in response to an initial determination thatan object exists within the off-center ROI. The object tracking may beapplied to the reconstructed ROI to refine or confirm, or both, theinitial object detection process.

A digital image acquisition device is also provided including anon-linear, wide-angled lens and an imaging sensor configured to capturedigital images of scenes containing one or more off-center peripheralregions, including an initial distorted image with a large field ofview, a processor, and a memory having code embedded therein forprogramming the processor to perform any of the methods describedherein.

One or more non-transitory, processor-readable storage media is/are alsoprovided having code embedded therein for programming a processor toperform any of the methods described herein.

Moreover, within a digital image acquisition system comprising anon-linear, wide-angled lens and an imaging sensor, a method is providedfor enhancing a scene containing one or more off-center peripheralregions, including acquiring an initial distorted image with a largefield of view, including using a non-linear, wide-angled lens andimaging sensor. The method includes determining and extracting anoff-center region of interest (hereinafter “ROI”) within said image.Geometric correction is applied to reconstruct the off-center ROI into arectangular or otherwise undistorted or less distorted frame ofreference as a reconstructed ROI. A quality of reconstructed pixelswithin said reconstructed ROI is determined. Image analysis isselectively applied to the reconstructed ROI based on the quality of thereconstructed pixels.

The method may include compensating for global motion of the imageacquisition system.

The method may also include repeating the method for a second distortedimage, and generating a second reconstructed ROI of approximately a sameportion of an image scene as the first reconstructed ROI. Responsive toanalysis of the first and second reconstructed ROIs, both the first andsecond reconstructed ROIs may be processed to generate an enhancedoutput image of substantially the same portion of the image scene.

The method may include, based on the selectively applying imageanalysis, adjusting an image acquisition parameter and repeating themethod for a second distorted image, and generating, based on the seconddistorted image, a second reconstructed ROI of approximately a sameportion of an image scene as the first reconstructed ROI. The first andsecond reconstructed ROIs of approximately the same portion of the imagescene may be processed to generate, based on the processing, an enhancedoutput image of substantially the same portion of the image scene.

Responsive to the analysis and the pixel quality, image enhancement maybe selectively applied to generate an enhanced output image.

A digital image acquisition device is also provided including anon-linear, wide-angled lens and an imaging sensor configured to capturedigital images of scenes containing one or more off-center peripheralregions, including an initial distorted image with a large field ofview, a processor, and a memory having code embedded therein forprogramming the processor to perform any of the methods describedherein.

One or more non-transitory, processor-readable storage media having codeembedded therein for programming a processor to perform any of themethods described herein.

In certain embodiments, the idea is to vary type and amount of imagecorrection depending on the location of the source image as well asdepending on a final projection of an image that was created byprojecting the source image (partially or whole) to a new coordinatesystem.

ROI Sub-Regions in Non-Linear Lens Geometries

Now certain embodiments are configured to address a different problem,namely that of tracking faces in off-center portions of the imaged areabased on a geometric correction engine and knowledge of one or moreregions of interest (ROIs) within the overall field of view of theimaging system which contain or contains at least one face. An exampleof three different ROIs of similar 4×3 “real” dimensions is illustratedin FIGS. 8( a)-8(b). Considering that the underlying image sensor maybe, for example, 3000×3000 pixels (9 Mpixel), then each of the regionsillustrated in FIGS. 8( a)-8(b) would be typically resolved to either aVGA (640×480) or SVGA (800×600) pixel resolution. However, there will bea difficult non-linear mapping of the actual pixels which are useful tothe final rectangular VGA, or SVGA image. While this mapping can beachieved using a geometric correction engine, given a knowledge of thelocation of the desired ROI, some pixels in the output VGA, or SVGAimage frame may be re-constructed from better initial data than others.

In certain embodiments, it may be an effect of the geometric remappingof the image scene, or portions thereof, that the removal of purplefringes (due to blue shift) or the correction of chromatic aberrationsmay be desired. US published patent application no. US2009/0189997 isincorporated by reference as disclosing embodiments to detect andcorrect purple fringing and chromatic aberrations in digital images.

Referring now to FIG. 9, a reconstructed image with two persons includedin the image scene is illustrated. Following the above discussion,different regions of this image will have different qualities ofreconstructed pixels. In some cases the pixel values in a region areextrapolated based on a lower amount of data from the image sensor. As ageneric measure, some pixels are described as having a reconstructionfactor of greater than one. This implies that these pixels arereconstructed with at least the equivalent of more than one pixel oforiginal data; while other pixels may have values less than unityimplying that they are reconstructed from less than one pixel oforiginal data.

Other factors may affect the quality of reconstruction. For example,regions with relatively homogeneous texture can be reconstructed withsignificantly less than 0.5 pixels of original data, whereas it may bedesired for regions with substantial fine detail to use greater than 1.0original pixel of equivalent data.

In certain embodiments, a geometric reconstruction engine can provideinformation on the quality of areas of the image, or even at the levelof individual pixels. In the example of FIG. 9, three regions areillustrated schematically. For the purposes of this illustrative,general example, these may be considered as representing pixelsreconstructed with significantly more than 1.0 original pixels (Region1, high quality—HQ); pixels reconstructed with the order of a singlepixel (Region 2, normal quality—NQ) and pixels with significantly lessthan 1.0 original pixels (Region 3, reduced quality—RQ). Inreconstructed images according to other embodiments, practically allpixels are HQ or NQ. However towards the periphery of the sensor, theremay be a significant proportion of the reconstructed image which is ofreduced quality. In the example of FIG. 9, two face regions areillustrated. One face belongs to a blue man that is entirely within a HQregion. The second face belonging to the yellow man has a face regionseparated into two regions: one lying in a region of normal pixelquality, and the second lying in a region of reduced quality. FIG. 10illustrates a sequence of ROIs tracked across an imaging sensor acquiredas a sequence of video frames.

Wide Field of View Optical System

As a wide field of view (WFOV) optical system may be configured to imagea horizontal field of >90-100 degrees or more, it may be desired toprocess the scene captured by the system to present an apparently“normal” perspective on the scene. There are several approaches to thisas exemplified by the example drawn from the architectural perspectiveof a long building described in Appendix A. In the context of our WFOVcamera this disclosure is primarily directed at considering how facialregions will be distorted by the WFOV perspective of this camera. Onecan consider such facial regions to suffer similar distortions to thefrontage of the building illustrated in this attached Appendix. Thus theproblem to obtain geometrically consistent face regions across theentire horizontal range of the WFOV camera is substantially similar tothe architectural problem described therein.

Thus, in order to obtain reasonable face regions, it is useful toalter/map the raw image obtained from the original WFOV horizontal sceneso that faces appear undistorted. Or in alternative embodiments faceclassifiers may be altered according to the location of the face regionswithin an unprocessed (raw) image of the scene.

In a first preferred embodiment the center region of the imagerepresenting up to 100′ of the horizontal field of view (FOV) is mappedusing a squeezed rectilinear projection. In a first embodiment this maybe obtained using a suitable non-linear lens design to directly projectthe center region of the scene onto the middle ⅔ of the image sensor.The remaining approximately ⅓ portion of the image sensor (i.e. ⅙ ateach end of the sensor) has the horizontal scene projected using acylindrical mapping. Again in a first preferred embodiment the edges ofthe wide-angle lens are designed to optically effect said projectiondirectly onto the imaging sensor. Thus, in a first embodiment, theentire horizontal scene is mapped onto the full extent of the imagesensor, as illustrated at FIG. 11( a).

Naturally the form and structure of such a complex hybrid optical lensmay not be conducive to mass production thus in an alternativeembodiment a more conventional rectilinear wide-angle lens is used andthe squeezing of the middle ⅔ of the image is achieved bypost-processing the sensor data. Similarly the cylindrical projectionsof the outer regions of the WFOV scene are performed by post processing.In this second embodiment the initial projection of the scene onto thesensor does not cover the full extent of the sensor and thus asignificant portion of the sensor area does not contain useful data. Theoverall resolution of this second embodiment is reduced and a largersensor would be used to achieve similar accuracy to the firstembodiment, as illustrated at FIG. 11( b).

In a third embodiment some of the scene mappings are achieved optically,but some additional image post-processing is used to refine the initialprojections of the image scene onto the sensor. In this embodiment thelens design can be optimized for manufacturing considerations, a largerportion of the sensor area can be used to capture useful scene data andthe software post-processing overhead is similar to the pure softwareembodiment.

In a fourth embodiment multiple cameras are configured to coveroverlapping portions of the desired field of view and the acquiredimages are combined into a single WFOV image in memory. These multiplecameras may be configured to have the same optical center, thusmitigating perspective related problems for foreground objects. In suchan embodiment techniques employed in panorama imaging may be usedadvantageously to join images at their boundaries, or to determine theoptimal join line where a significant region of image overlap isavailable. The following cases belong to the same assignee and relate topanorama imaging and are incorporated by reference: U.S. Ser. Nos.12/636,608, 12/636,618, 12/636,629, 12/636,639, and 12/636,647, as areUS published apps nos. US2006/0182437, US2009/0022422, US2009/0021576and US2006/0268130.

In one preferred embodiment of the multi-camera WFOV device three, ormore standard cameras with a 60 degree FOV are combined to provide anoverall horizontal WFOV of 120-150 degrees with an overlap of 15-30degrees between cameras. The field of view for such a cameras can beextended horizontally by adding more cameras; it may be extendedvertically by adding an identical array of 3 or more horizontallyaligned cameras facing in a higher (or lower) vertical direction andwith a similar vertical overlap of 15-30 degrees offering a vertical FOVof 90-105 degrees for two such WFOV arrays. The vertical FOV may beincreased by adding further horizontally aligned cameras arrays. Suchconfigurations have the advantage that all individual cameras can beconventional wafer-level cameras (WLC) which can be mass-produced.

In an alternative multi-cameras embodiment a central WFOV cameras hasits range extended by two side-cameras. The WFOV cameras can employ anoptical lens optimized to provide a 120 degree compressed rectilinearmapping of the central scene. The side cameras can be optimized toprovide a cylindrical mapping of the peripheral regions of the scene,thus providing a similar result to that obtained in FIG. 3( a), butusing three independent cameras with independent optical systems ratherthan a single sensor/ISP as shown in FIG. 3( b). Again techniquesemployed in panorama imaging to join overlapping images can beadvantageously used (see the Panorama cases referred to above herein).

After image acquisition and, depending on the embodiment, additionalpost-processing of the image, we arrive at a mapping of the image scenewith three main regions. Over the middle third of the image there is anormal rectilinear mapping and the image is undistorted compared to astandard FOV image; over the next ⅓ of the image (i.e. ⅙ of image oneither side) the rectilinear projection becomes increasingly squeezed asillustrated in FIGS. 1A-1G; finally, over the outer approximately ⅓ ofthe image a cylindrical projection, rather than rectilinear is applied.

FIG. 3( a) illustrates one embodiment where this can be achieved using acompressed rectilinear lens in the middle, surrounded by two cylindricallenses on either side. In a practical embodiment all three lenses couldbe combined into a single lens structure designed to minimizedistortions where the rectilinear projection of the original sceneoverlaps with the cylindrical projection.

A standard face-tracker can now be applied to the WFOV image as all faceregions should be rendered in a relatively undistorted geometry.

In alternative embodiments the entire scene need not be re-mapped, butinstead only the luminance components are re-mapped and used to generatea geometrically undistorted integral image. Face classifiers are thenapplied to this integral image in order to detect faces. Once faces aredetected those faces and their surrounding peripheral regions can bere-mapped on each frame, whereas it may be sufficient to re-map theentire scene background, which is assumed to be static, onlyoccasionally, say every 60-120 image frames. In this way imageprocessing and enhancement can be focused on the people in the imagescene.

In alternative embodiments it may not be desirable to completely re-mapthe entire WFOV scene due to the computational burden involved. In suchembodiment, referring to U.S. Pat. Nos. 7,460,695, 7,403,643, 7,565,030,and 7,315,631 and US published app no. US2009/0263022, which areincorporated by reference along with US2009/0179998, US2009/0080713, US2009/0303342 and U.S. Ser. No. 12/572,930, filed Oct. 2, 2009 by thesame assignee. These references describe predicting face regions(determined from the previous several video frames). The images may betransformed using either cylindrical or squeezed rectilinear projectionprior to applying a face tracker to the region. In such an embodiment,it may be involved from time to time to re-map a WFOV in order to makean initial determination of new faces within the WFOV image scene.However, after such initial determination only the region immediatelysurrounding each detected face need be re-mapped.

In certain embodiments, the remapping of the image scene, or portionsthereof, involves the removal of purple fringes (due to blue shift) orthe correction of chromatic aberrations. The following case belongs tothe same assignee is incorporated by reference and relates to purplefringing and chromatic aberration correction: US2009/0189997.

In other embodiments a single mapping of the input image scene is used.If, for example, only a simple rectilinear mapping were applied acrossthe entire image scene the edges of the image would be distorted andonly across the middle 40% or so of the image can a conventional facetracker be used. Accordingly the rectangular classifiers of the facetracker are modified to take account of the scene mappings across theother 60% of image scene regions: Over the middle portion of the imagethey can be applied unaltered; over the second 30% they are selectivelyexpanded or compressed in the horizontal direction to account for thedegree of squeezing of the scene during the rectilinear mapping process.Finally, in the outer ⅓ the face classifiers are adapted to account forthe cylindrical mapping used in this region of the image scene.

In order to transform standard rectangular classifiers of a particularsize, say 32×32 pixels, it may be advantageous in some embodiments toincrease the size of face classifiers to, for example, 64×64. Thislarger size of classifier would enable greater granularity, and thusimproved accuracy in transforming normal classifiers to distorted ones.This comes at the expense of additional computational burden for theface tracker. However we note that face tracking technology is quitebroadly adopted across the industry and is known as a robust and welloptimized technology. Thus the trade off of increasing classifiers from32×32 to 64×64 for such faces should not cause a significant delay onmost camera or smartphone platforms. The advantage is that pre-existingclassifier cascades can be re-used, rather than having to train new,distorted ones.

Having greater granularity for the classifiers is advantageousparticularly when starting to rescale features inside the classifierindividually, based on the distance to the optical center. In anotherembodiment, one can scale the whole 22×22 (this is a very good size forface classifiers) classifier with fixed dx,dy (computed as distance fromthe optical center). Having larger classifiers does not put excessivestrain on the processing. Advantageously, it is opposite to that,because there are fewer scales to cover. In this case, the distance tosubject is reduced. In an alternative embodiment an initial, shortenedchain of modified classifiers is applied to the raw image (i.e. withoutany rectilinear or cylindrical re-mapping). This chain is composed ofsome of the initial face classifiers from a normal face detection chain.These initial classifiers are also, typically, the most aggressive toeliminate non-faces from consideration. These also tend to be simpler inform and the first four Haar classifiers from the Viola-Jones cascadeare illustrated in FIG. 4 (these may be implemented through a 22×22pixel window in another embodiment).

Where a compressed rectilinear scaling would have been employed (asillustrated in FIG. 1F, it is relatively straightforward to invert thisscaling and expand (or contract) these classifiers in the horizontaldirection to compensate for the distortion of faces in the raw imagescene. (In some embodiments where this distortion is cylindrical towardsthe edges of the scene then classifiers may need to be scaled both inhorizontal and vertical directions). Further, it is possible from aknowledge of the location at which each classifier is to be applied and,optionally, the size of the detection window, to perform the scaling ofthese classifiers dynamically. Thus only the original classifiers haveto be stored together with data on the required rectilinear compressionfactor in the horizontal direction. The latter can easily be achievedusing a look-up table (LUT) which is specific to the lens used.

This short classifier chain is employed to obtain a set of potentialface regions which may then be re-mapped (using, for example, compressedrectilinear compression and/or cylindrical mapping) to enable theremainder of a complete face detection classifier chain to be applied toeach potential face region. This embodiment relies on the fact that99.99% of non-face regions are eliminated by applying the first few faceclassifiers; thus a small number of potential face regions would bere-mapped rather than the entire image scene before applying a full facedetection process.

In another embodiment, distortion may be compensated by a method thatinvolves applying geometrical adjustments (function of distance tooptical center) when an integral image is computed (in the cases wherethe template matching is done using II) or compensate for the distortionwhen computing the sub-sampled image used for face detection and facetracking (in the cases where template matching is done directly on Ydata).

Note that face classifiers can be divided into symmetric andnon-symmetric classifiers. In certain embodiments it may be advantageousto use split classifier chains. For example right and left-hand facedetector cascades may report detection of a half-face region—this mayindicate that a full face is present but the second half is more or lessdistorted than would be expected, perhaps because it is closer to orfarther from the lens than is normal. In such cases a more relaxed half,or full-face detector may be employed to confirm if a full face isactually present or a lower acceptance threshold may be set for thecurrent detector. The following related apps belong to the same assigneeare incorporated by reference: US2007/0147820, US2010/0053368,US2008/0205712, US2009/0185753, US2008/0219517 and US2010/0054592, andU.S. Ser. No. 61/182,625, filed May 29, 2009 and U.S. Ser. No.61/221,455, filed Jun. 29, 2009.

Scene Enhancements

In certain embodiments, a first image of a scene is reconstructed fromsensor data. This first image is then analyzed using a variety of imageanalysis techniques and at least a second set of main image data isacquired and used to reconstruct at least a second image ofsubstantially the same scene. The second image is then analyzed and theresults of these at least two analyses are used to create an enhancedimage of the original scene. Examples of various image analysistechniques include: (i) foreground/background separation; (ii) facedetection and facial feature detection including partial or occludedfaces or features and peripheral face regions; (iii) indoor/outdoorimage classification; (iv) global luminance analysis; (v) localluminance analysis; (vi) directional luminance analysis; (vii) imageblur analysis—global and local; (viii) image gradient analysis; (ix)color filtering & segmentation including color correlogram analysis; (x)image variance analysis; (xi) image texture filtering & segmentation.

The following belong to the same assignee as the present application andare incorporated by reference, particularly as describing alternativeembodiments:

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U.S. patent application Ser. Nos. 13/077,936 and 13/077,891 are alsoincorporated by reference as disclosing alternative embodiments.

Face Detectors

In the following examples, embodiments involving a rectangular facedetector will be described. However, the invention is not limited todetecting faces, and other objects may be detected, and such objects mayalso be tracked. Thus, where face detection or face tracking ismentioned herein, it is to be understood that the described features maybe applied to objects other than faces. The face or other objectdetector may be based on variations of the Viola-Jones method where acascade of rectangular classifiers is applied in sequence to a testregion in the integral-image domain. Some approaches use a pass/failcascade, while others employ a cumulative probability which allows thetest region to fall below acceptance for some classifiers as long as itcompensates by scoring above a threshold for the majority of theclassifiers in the cascade.

Different types of classifiers may be used in a cascade. For example,one combination uses Haar-classifiers (see FIG. 12) for the initialstages and applies more granular Census-classifiers in the later stagesof the cascade. Split cascades may be used where several short cascadesare used to test for a predetermined condition, such as facial pose ordirectional lighting condition (see, e.g., US2008/0219517, incorporatedby reference. One may also test for half-faces (see, e.g., U.S. Ser.Nos. 12/790,594 and 12/825,280, incorporated by reference. Facedetection may be applied to images obtained from non-linear cylindricallenses or combinations.

For lenses and techniques describing such systems and a range ofembodiments, see U.S. Ser. No. 12/959,089, incorporated by reference.

Embodiments are described above and below herein involving face or otherobject detection in a portion of an image acquired with a nonlinear lenssystem. Typically the region of interest, or ROI, lies in the peripheryof an ultra wide-angle lens such as a fish-eye with field of view, orFOV, of upwards of 180 degrees or greater. A geometric correction enginemay be pre-calibrated for the particular lens in use.

In accordance with certain embodiments, a main image is acquired, andmapped onto an image sensor by a non-linear lens creating a distortedrepresentation of the image scene. This distortion can be, for example,any of the types illustrated at FIG. 3, 4(c), 4(f) or 4(i). A trackingsystem follows some activity or events within the main FOV anddetermines one or more regions of interest within that FOV. In certainembodiments, the tracking system includes either a face detector, amotion detector or a person detector, or other object detector such as avehicle detector, animal detector or for sporting events a ball orracket detector may be used. In a home environment, hand or headdetectors may be used to track gestures, e.g., on devices such as theWii or the Playstation. Movement may simplify the tracking by providingan easily distinguished object, even in a distorted original image. Incertain embodiments, a first original image frame may be processed by ageometric reconstruction engine to output the currently tracked ROI(s).A quality map of each reconstructed ROI is also provided.

As the full image frame is not processed, this is significantly fasterthan applying the engine to the entire acquired, distorted originalimage frame. This is highly advantageous for portable and even handhelddevices, wherein efficient use of computational resources is at apremium.

Face Tracking After Applying the Geometric Correction Engine

In one embodiment, the relevant ROI is reconstructed from the maindistorted image and regions of different quality are determined. Ameasure of reconstructed pixel quality may be available. The image ispartitioned into a number of regions of differing quality. A number offace (or other object) detector cascades of varying granularity are alsoavailable. In one embodiment, several cascades of different sizedclassifiers are available e.g. 32×32, 24×24 and 14×14 pixel classifiers.

In an alternative, but related embodiment, a hardware resizing engine isused to upscale or downscale the ROI image to match with a fixed sizeface detector cascade, say 22×22 pixel, but having the same effect asapplying different sizes of cascaded classifiers. See U.S. Pat. Nos.7,460,695, 7,403,643 and 7,315,631, incorporated by reference, fordetailed explanations of advantageous face detecting and trackingembodiments. Once a face (or other object) is detected, a history ofthat face may be recorded over a sequence of image frames and on eachnew frame acquisition a face candidate area is marked indicating aregion of the frame where there is a very high probability of finding aface because a face was detected at or near the center of this region inthe previous image frame, or some estimated movement distance from whereit was detected in the previous frame. According to this embodiment, aface-detection/tracking process is next applied to the reconstructed ROIimage. This process may be modified according to the determined pixelquality of different portions of the image. Thus in regions where theimage quality is high quality, or HQ, and normal quality, or NQ, allthree sizes of face detector may be used in the face detection/trackingprocess. However in regions of reduced quality, or RQ, there maytypically not be sufficient pixel resolution to use the smaller size(s)of face classifier. The face detection/tracking process in accordancewith certain embodiments determines these regions and understands not toapply smaller classifiers thus eliminating potential false positives andsaving time.

A particular complication arises where a face region overlaps betweentwo different regions of image quality as illustrated in FIG. 9, wherethe person on the right is partly in a NQ region and partly in a RQregion. To handle such cases the face detection/tracking process allowsthe detection window to overlap into the RQ region but applies a morerelaxed thresholding or a shorter classifier chain to compensate for thepossibility that the pixel quality/resolution in the RQ region may notbe good enough to confirm a face region. In certain embodiments, thedetection window will not be allowed to overlap more than 50% into theRQ region, although if the tracking process indicates the likelihood ofa face region from a previous image frame, then it may extend up to 75%into that region. In alternative embodiments, face candidate areaswithin the HQ region may be reconstructed at a higher pixel resolutionto enable more granular (smaller) face detectors to be applied if theface history indicates the person is moving into the background.

Face Tracking Without On-Demand Superresolution Enhancement

In certain embodiments, the smaller sizes of face detector will not beapplied to the RQ (reduced quality) regions of the image. However, inother embodiments, (i) the device determines whether a tracked objectregion is likely to have moved into a RQ region of the image, and (ii)the device determines whether a new face of small size is likely to haveentered the image frame. If neither of these applies, then in certainembodiments the RQ region will not be searched and no action will betaken.

If, however, the tracking history, or some knowledge of the regionappearing in the RQ portion of the image (e.g., that a door is locatedin that area) suggests that it is desirable to search in this region,then the face or other object detection module and/or face or otherobject tracking module may be used to initiate enhancement of the RQportions of the image frame as follows: Referring back now to FIG. 8, itis recognized that it is possible to acquire an underlying HD imagestream at 60-120 fps even with current consumer imaging technology, andfaster acquisition rates are likely in the near term. Thus, in certainembodiments several full size images may be acquired in the sametimeframe as a single frame of 30 fps video data. In certainembodiments, only a portion, 75% or less, and even as little as 50% or30% or even 20% or less, or even 10%, of each full frame correspondingto the current ROI is processed by the geometric reconstruction engine.In these embodiments, several, nearly contemporaneous reconstructedimage frames are obtained of a particular ROI. There may (very likely)be slight misalignments between such frames but as our intention is toenhance the lower quality regions of the ROI, such slight misalignmentsare, in fact, desirable.

Responsive to an indication from the face or other objectdetection/tracking subsystem, additional image frames are acquired incertain embodiments with a close temporal proximity to the originalacquisition. Depending on the level of desired quality and the speed atwhich image frames can be acquired, at least 2, and even 4-8, or perhapsmore, additional image frames may be obtained. After each acquisitionthe main image buffer may be cleared as in certain embodiments only theextracted ROI(s) are buffered.

Super-resolution processing is then applied in certain embodiments togenerate a single, enhanced output ROI for each sequence of extractedROI(s). Referring to FIG. 7, as the peripheral regions of the main imageare less sensitive to global image motion, motion compensation can beoptionally performed or even avoided altogether. Any small motioneffects will tend to improve the results from super-resolutionprocessing as some offsets in alignment between the images are desiredin advantageous embodiments involving application of one or moresuper-resolution techniques. After processing and enhancing relevantimage region(s), additional face or other object detection and/ortracking may be performed using relatively smaller classifiers, e.g.22×22 or 14×14.

Two Stage Face Tracking

In certain embodiments, geometric correction is applied to each ROIwithin a main acquired image prior to applying face or other objectdetection and/or tracking. A distorted scene may be “undistorted,” incertain embodiments, and in other embodiments, distortion is actuallyapplied to the face or other object classifiers, such that they may beapplied to raw image data. In the latter embodiments, the resultingclassifiers are non-rectangular. For the more non-linear regions towardsthe periphery of the image sensor, it becomes increasingly complicatedto apply modified classifiers within a cascade in a consistent manner.Thus, the use of classifiers in these embodiments may be similar to ordiffer somewhat from the use of classifiers described in U.S. Ser. Nos.12/959,089, 12/959,137, and 12/959,131, which belong to the sameassignee and are incorporated by reference. These describe use ofcylindrical and hybrid rectangular-cylindrical classifiers.

Now in order to transform standard rectangular classifiers of aparticular size, say 32×32 pixels, it is advantageous in someembodiments to increase the size of face classifiers to, for example,64×64. This larger size of classifier enables greater granularity, andthus improved accuracy, in transforming normal classifiers to distortedones, particularly for the most distorted regions towards the peripheryof the imaging sensor. The advantage is that preexisting classifiercascades can be re-used, rather than having to train new, distortedones.

In certain embodiments, an initial, shortened chain of modifiedclassifiers is applied to the raw image. This approach is particularlyadvantageous when it is not practical to perform a full face or otherobject detection on an uncorrected ROI. In this embodiment, an initialdetection of likely face or other object regions is performed on theuncorrected raw image. Subsequently, in regions where a face isinitially detected, the uncorrected ROI will be passed to the geometriccorrection engine and transformed into a rectangular frame-of-referencewhere further face or other object detection will be completed using amore straight-forward cascade of rectangular classifiers.

A shortened, distorted classifier chain in accordance with certainembodiments may be composed of the first few face or other objectclassifiers from a normal face or other object detection chain. Theseinitial classifiers may be the most aggressive to eliminate non-facesfrom consideration. These may also tend to be less complex in form, suchas the first four Haar classifiers from the Viola-Jones cascade that areillustrated in FIG. 12. These may be implemented through a 22×22 pixelwindow in a practical embodiment). Further, it is advantageous incertain embodiments based on a knowledge of the location at which eachclassifier is to be applied and, optionally, the size of the detectionwindow, to perform scaling of one or more classifiers dynamically. Incertain embodiments, this is achieved using the geometric correctionengine in reverse. Thus in these embodiments, the original classifiersmay be stored together with data on the rectilinear scaling factors inhorizontal and/or radial directions. In alternative embodiments, polarscaling factors may be employed. These can be achieved using a look-uptable (LUT) which is specific to the lens used if the relevanttransformations cannot be achieved using the geometric correctionengine.

This short classifier chain is employed in certain embodiments to obtaina set of potential face regions which may then be re-mapped, using, forexample, compressed rectilinear and/or cylindrical mapping, to enablethe remainder of a complete face or other object detection classifierchain to be applied to each potential face or other object region. Thisembodiment is particularly advantageous when a large percentage, such asmore than 95% or even more than 98% or 99%, or even 99.99%, of non-faceor non-object regions may be eliminated by applying the first few faceor other object classifiers. Thus, it is advantageous in certainembodiments to re-map a small number of potential face or other objectregions, rather than the entire image scene, before applying a full faceor other object detection process. This is approach is particularlyuseful in applications where it is only desired to transform and analyzethe tracked face or other object region or regions, e.g., securityapplications. In certain embodiments, it is optional to correct or notcorrect portions of the image scene around a tracked individual orobject, such that significant computational savings are achieved.

In certain embodiments, face classifiers are divided into symmetric andnon-symmetric classifiers (see e.g., U.S. Ser. No. 61/417,737,incorporated by reference). In certain embodiments, it may beadvantageous to use split classifier chains. For example, right andleft-hand face or other object detector cascades may report detection ofa half-face or other object region. This may be used to indicate that afull face is present, while the second half may be more or lessdistorted than would be expected, in one example because it may becloser to or farther from the lens than is normal. In such cases, morerelaxed half- or full-face or other object detector may be employed toconfirm whether a full face or other object is actually present and/orwhether a lower acceptance threshold may be set for the current detector(see, e.g., U.S. Pat. No. 7,692,696, and US published applications nos.2011/0050938, 2008/0219517 and 2008/0205712, and U.S. Ser. Nos.13/020,805, 12/959,320, 12/825,280, 12/572,930, 12/824,204 and12/944,701, which belong to the same assignee and are incorporated byreference).

In certain embodiments, the entire scene is not re-mapped, and insteadonly the luminance components are remapped and used to generate ageometrically undistorted integral image. Face or other objectclassifiers are then applied to this integral image in order to detectfaces or other objects respectively. Once faces or other objects aredetected those faces or other objects, with or without their surroundingperipheral regions, on each frame. In certain embodiments, it may besufficient to re-map the entire scene background, which can be assumedin these embodiments to be static, only occasionally, say every 60-120image frames. Image processing and enhancement is focused in certainembodiments on the people, faces or other objects of interest in theimage scene. In alternative embodiments, to save computationalresources, only one or more portions of the scene are re-mapped. In suchembodiments, only the predicted face candidate areas, determined fromone or more or several previous frames (see U.S. Pat. No. 7,460,695,incorporated by reference), may be transformed by the geometriccorrection engine, prior to applying a face or other object tracker tothe region. In these embodiments, the entire main acquired image may befully re-mapped at selected times in order to make an initialdetermination of new faces or other tracked objects within the entireimage scene. After such initial determination, only the regionimmediately surrounding each detected face or other object is generallyre-mapped.

In certain embodiments, when a face is tracked across the scene, it maybe desired to draw particular attention to that face and to emphasize itagainst the main scene. In one exemplary embodiment, suitable forapplications in video telephony, there may be one or more faces in themain scene, while one (or more!) of these may be speaking In this case,a stereo microphone may be used to locate the speaking face. This faceregion, and optionally other foreground regions (e.g., neck, shoulders &torso, shirt, chair-back, and/or desk-top) may be further processed tomagnify them (e.g., by a factor of ×1.8 times) against the background.In certain embodiments, the magnified face may be composited onto thebackground image in the same location as the unmagnified original. Inanother embodiment, the other faces and the main background of the imageare de-magnified and/or squeezed in order to keep the overall image sizeself-consistent. This may lead to some image distortion, particularlysurrounding the “magnified” face. This can help to emphasize the personspeaking (see, e.g., FIG. 4). In this case, the degree of magnificationmay be generally <×1.5 to avoid excessive distortion across theremainder of the image.

Embodiments have been described as including various operations. Many ofthe processes are described in their most basic form, but operations canbe added to or deleted from any of the processes without departing fromthe scope of the invention.

The operations of the invention may be performed by hardware componentsor may be embodied in machine-executable instructions, which may be usedto cause a general-purpose or special-purpose processor or logiccircuits programmed with the instructions to perform the operations.Alternatively, the steps may be performed by a combination of hardwareand software. The invention may be provided as a computer programproduct that may include a machine-readable medium having stored thereoninstructions, which may be used to program a computer (or otherelectronic devices) to perform a process according to the invention. Themachine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs, and magneto-optical disks, ROMs,RAMs, EPROMs, EEPROMs, magnet or optical cards, flash memory, or othertype of media/machine-readable medium suitable for storing electronicinstructions. Moreover, the invention may also be downloaded as acomputer program product, wherein the program may be transferred from aremote computer to a requesting computer by way of data signals embodiedin a carrier wave or other propagation medium via a communication cell(e.g., a modem or network connection). All operations may be performedat the same central site or, alternatively, one or more operations maybe performed elsewhere.

While an exemplary drawings and specific embodiments of the presentinvention have been described and illustrated, it is to be understoodthat that the scope of the present invention is not to be limited to theparticular embodiments discussed. Thus, the embodiments shall beregarded as illustrative rather than restrictive, and it should beunderstood that variations may be made in those embodiments by workersskilled in the arts without departing from the scope of the presentinvention.

In addition, in methods that may be performed according to preferredembodiments herein and that may have been described above, theoperations have been described in selected typographical sequences.However, the sequences have been selected and so ordered fortypographical convenience and are not intended to imply any particularorder for performing the operations, except for those where a particularorder may be expressly set forth or where those of ordinary skill in theart may deem a particular order to be necessary.

In addition, all references cited above and below herein, as well as thebackground, invention summary, abstract and brief description of thedrawings, are all incorporated by reference into the detaileddescription of the preferred embodiments as disclosing alternativeembodiments.

1. Within an image acquisition system comprising a non-linear,wide-angled lens and an imaging sensor, a method of enhancing a scenecontaining one or more off-center peripheral regions, the methodcomprising: acquiring an initial distorted image with a large field ofview, including using a non-linear, wide-angled lens and imaging sensor;determining and extracting an off-center region of interest (hereinafter“ROI”) within said image; applying geometric correction to reconstructthe off-center ROI into an approximately rectangular frame of referenceas a first reconstructed ROI; determining a quality of reconstructedpixels within said first reconstructed ROI; applying object tracking toone or more regions within the first reconstructed ROI respectivelyadapted to one or more local reconstructed pixel qualities within theone or more regions; determining if undetected objects below apredetermined size threshold are likely to exist in one or more reducedquality regions of the first reconstructed ROI; responsive to saiddetermining if undetected objects exist, acquiring one or moreadditional initially distorted images, and extracting and reconstructingmatching additional ROIs to combine with reduced quality pixels of saidfirst reconstructed ROI to provide one or more enhanced ROIs. performinga further action based on a value of a parameter of an object within theone or more enhanced ROIs.
 2. The method of claim 1, further comprisingusing a super-resolution technique to generate at least one of said oneor more enhanced ROIs.
 3. The method of claim 1, further comprisingapplying additional object tracking to the enhanced ROI to confirm alocation of a object below said predetermined size threshold.
 4. Amethod as in claim 1, further comprising compensating for global motionof the imaging device.
 5. A method as in claim 1, wherein the parametercomprises location.
 6. A method as in claim 1, further comprising, priorto said applying geometric correction, applying an initial objectdetection process to the off-center ROI.
 7. A method as in claim 6,wherein said applying geometric correction is performed in response toan initial determination that an object exists within the off-centerROI.
 8. A method as in claim 7, wherein said object tracking is appliedto the reconstructed ROI to refine or confirm, or both, the initialobject detection process.
 9. A digital image acquisition device,comprising: a non-linear, wide-angled lens and an imaging sensorconfigured to capture digital images of scenes containing one or moreoff-center peripheral regions, including an initial distorted image witha large field of view; a processor; a memory having code embeddedtherein for programming the processor to perform a method of enhancing ascene containing one or more off-center peripheral regions, wherein themethod comprises: acquiring an initial distorted image with a largefield of view, including using a non-linear, wide-angled lens andimaging sensor; determining and extracting an off-center region ofinterest (hereinafter “ROI”) within said image; applying geometriccorrection to reconstruct the off-center ROI into an approximatelyrectangular frame of reference as a first reconstructed ROI; determininga quality of reconstructed pixels within said first reconstructed ROI;applying object tracking to one or more regions within the firstreconstructed ROI respectively adapted to one or more localreconstructed pixel qualities within the one or more regions;determining if undetected objects below a predetermined size thresholdare likely to exist in one or more reduced quality regions of the firstreconstructed ROI; responsive to said determining if undetected objectsexist, acquiring one or more additional initially distorted images, andextracting and reconstructing matching additional ROIs to combine withreduced quality pixels of said first reconstructed ROI to provide one ormore enhanced ROIs. performing a further action based on a value of aparameter of an object within the one or more enhanced ROIs.
 10. Thedevice of claim 9, wherein the method further comprises using asuper-resolution technique to generate at least one of said one or moreenhanced ROIs.
 11. The device of claim 9, wherein the method furthercomprises applying additional object tracking to the enhanced ROI toconfirm a location of a object below said predetermined size threshold.12. A device as in claim 9, wherein the method further comprisescompensating for global motion of the imaging device.
 13. A device as inclaim 9, wherein the parameter comprises location.
 14. A device as inclaim 9, wherein the method further comprises, prior to said applyinggeometric correction, applying an initial object detection process tothe off-center ROI.
 15. A device as in claim 14, wherein said applyinggeometric correction is performed in response to an initialdetermination that an object exists within the off-center ROI.
 16. Adevice as in claim 15, wherein said object tracking is applied to thereconstructed ROI to refine or confirm, or both, the initial objectdetection process.
 17. One or more non-transitory, processor-readablestorage media having code embedded therein for programming a processorto perform a method of enhancing a scene captured with a non-linear,wide-angled lens and containing one or more off-center peripheralregions, wherein the method comprises: determining and extracting anoff-center region of interest (hereinafter “ROI”) within an acquiredimage; applying geometric correction to reconstruct the off-center ROIinto an approximately rectangular frame of reference as a firstreconstructed ROI; determining a quality of reconstructed pixels withinsaid first reconstructed ROI; applying object tracking to one or moreregions within the first reconstructed ROI respectively adapted to oneor more local reconstructed pixel qualities within the one or moreregions; determining if undetected objects below a predetermined sizethreshold are likely to exist in one or more reduced quality regions ofthe first reconstructed ROI; responsive to said determining ifundetected objects exist, acquiring one or more additional initiallydistorted images, and extracting and reconstructing matching additionalROIs to combine with reduced quality pixels of said first reconstructedROI to provide one or more enhanced ROIs. performing a further actionbased on a value of a parameter of an object within the one or moreenhanced ROIs.
 18. One or more non-transitory processor-readable storagemedia as in claim 17, wherein the method further comprises using asuper-resolution technique to generate at least one of said one or moreenhanced ROIs.
 19. One or more non-transitory processor-readable storagemedia as in claim 17, wherein the method further comprises applyingadditional object tracking to the enhanced ROI to confirm a location ofa object below said predetermined size threshold.
 20. One or morenon-transitory processor-readable storage media as in claim 17, whereinthe method further comprises compensating for global motion of theimaging device.
 21. One or more non-transitory processor-readablestorage media as in claim 17, wherein the parameter comprises location.22. One or more non-transitory processor-readable storage media as inclaim 17, wherein the method further comprises, prior to said applyinggeometric correction, applying an initial object detection process tothe off-center ROI.
 23. One or more non-transitory processor-readablestorage media as in claim 22, wherein said applying geometric correctionis performed in response to an initial determination that an objectexists within the off-center ROI.
 24. One or more non-transitoryprocessor-readable storage media as in claim 23, wherein said objecttracking is applied to the reconstructed ROI to refine or confirm, orboth, the initial object detection process.